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International Journal for Uncertainty Quantification
IF: 0.967 5-Year IF: 1.301 SJR: 0.531 SNIP: 0.8 CiteScore™: 1.52

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2016015984
pages 109-126

INCORPORATING PRIOR KNOWLEDGE FOR QUANTIFYING AND REDUCING MODEL-FORM UNCERTAINTY IN RANS SIMULATIONS

Jianxun Wang
Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, Virginia 24060, USA
Jin-Long Wu
Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, Virginia 24060, USA
Heng Xiao
Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, Virginia 24060, USA

ABSTRACT

Simulations based on Reynolds-averaged Navier-Stokes (RANS) models have been used to support high-consequence decisions related to turbulent flows. Apart from the deterministic model predictions, the decision makers are often equally concerned about the prediction confidence. Among the uncertainties in RANS simulations, the model-form uncertainty is an important or even a dominant source. Therefore, quantifying and reducing the model-form uncertainties in RANS simulations are of critical importance to make risk-informed decisions. Researchers in statistics communities have made efforts on this issue by considering numerical models as black boxes. However, this physics-neutral approach is not a most efficient use of data, and is not practical for most engineering problems. Recently, we proposed an open-box, Bayesian framework for quantifying and reducing model-form uncertainties in RANS simulations based on observation data and physics-prior knowledge. It can incorporate the information from the vast body of existing empirical knowledge with mathematical rigor, which enables a more efficient usage of data. In this work, we examine the merits of incorporating various types of prior knowledge in the uncertainties quantification and reduction in RANS simulations. The result demonstrates that informative physics-based prior knowledge plays an important role in improving the performance of model-form uncertainty reduction, particularly when the observation data are limited. Moreover, it suggests that the proposed Bayesian framework is an effective way to incorporate empirical knowledge from various sources of turbulence modeling.